Kazimierz Banasik

Bio: Kazimierz Banasik is an academic researcher from Warsaw University of Life Sciences. The author has contributed to research in topics: Surface runoff & Runoff curve number. The author has an hindex of 16, co-authored 74 publications receiving 879 citations.

Monthly Rainfall Erosivity: Conversion Factors for Different Time Resolutions and Regional Assessments

Abstract: As a follow up and an advancement of the recently published Rainfall Erosivity Database at European Scale (REDES) and the respective mean annual R-factor map, the monthly aspect of rainfall erosivity has been added to REDES. Rainfall erosivity is crucial to be considered at a monthly resolution, for the optimization of land management (seasonal variation of vegetation cover and agricultural support practices) as well as natural hazard protection (landslides and flood prediction). We expanded REDES by 140 rainfall stations, thus covering areas where monthly R-factor values were missing (Slovakia, Poland) or former data density was not satisfactory (Austria, France, and Spain). The different time resolutions (from 5 to 60 min) of high temporal data require a conversion of monthly R-factor based on a pool of stations with available data at all time resolutions. Because the conversion factors show smaller monthly variability in winter (January: 1.54) than in summer (August: 2.13), applying conversion factors on a monthly basis is suggested. The estimated monthly conversion factors allow transferring the R-factor to the desired time resolution at a European scale. The June to September period contributes to 53% of the annual rainfall erosivity in Europe, with different spatial and temporal patterns depending on the region. The study also investigated the heterogeneous seasonal patterns in different regions of Europe: on average, the Northern and Central European countries exhibit the largest R-factor values in summer, while the Southern European countries do so from October to January. In almost all countries (excluding Ireland, United Kingdom and North France), the seasonal variability of rainfall erosivity is high. Very few areas (mainly located in Spain and France) show the largest from February to April. The average monthly erosivity density is very large in August (1.67) and July (1.63), while very small in January and February (0.37). This study addresses the need to develop monthly calibration factors for seasonal estimation of rainfall erosivity and presents the spatial patterns of monthly rainfall erosivity in European Union and Switzerland. Moreover, the study presents the regions and seasons under threat of rainfall erosivity.

Bayesian uncertainty assessment of flood predictions in ungauged urban basins for conceptual rainfall-runoff models

Abstract: Urbanization and the resulting land-use change strongly affect the water cycle and runoff-processes in wa- tersheds. Unfortunately, small urban watersheds, which are most affected by urban sprawl, are mostly ungauged. This makes it intrinsically difficult to assess the consequences of urbanization. Most of all, it is unclear how to reliably assess the predictive uncertainty given the structural deficits of the applied models. In this study, we therefore investigate the un- certainty of flood predictions in ungauged urban basins from structurally uncertain rainfall-runoff models. To this end, we suggest a procedure to explicitly account for input uncer- tainty and model structure deficits using Bayesian statistics with a continuous-time autoregressive error model. In ad- dition, we propose a concise procedure to derive prior pa- rameter distributions from base data and successfully apply the methodology to an urban catchment in Warsaw, Poland. Based on our results, we are able to demonstrate that the au- toregressive error model greatly helps to meet the statisti- cal assumptions and to compute reliable prediction intervals. In our study, we found that predicted peak flows were up to 7 times higher than observations. This was reduced to 5 times with Bayesian updating, using only few discharge mea- surements. In addition, our analysis suggests that imprecise rainfall information and model structure deficits contribute mostly to the total prediction uncertainty. In the future, flood predictions in ungauged basins will become more important due to ongoing urbanization as well as anthropogenic and climatic changes. Thus, providing reliable measures of un- certainty is crucial to support decision making.

Considering rating curve uncertainty in water level predictions

Abstract: Streamflow cannot be measured directly and is typically derived with a rating curve model. Unfortunately, this causes uncertainties in the streamflow data and also in- fluences the calibration of rainfall-runoff models if they are conditioned on such data. However, it is currently unknown to what extent these uncertainties propagate to rainfall-runoff predictions. This study therefore presents a quantitative ap- proach to rigorously consider the impact of the rating curve on the prediction uncertainty of water levels. The uncer- tainty analysis is performed within a formal Bayesian frame- work and the contributions of rating curve versus rainfall- runoff model parameters to the total predictive uncertainty are addressed. A major benefit of the approach is its inde- pendence from the applied rainfall-runoff model and rating curve. In addition, it only requires already existing hydro- metric data. The approach was successfully demonstrated on a small catchment in Poland, where a dedicated monitoring campaign was performed in 2011. The results of our case study indicate that the uncertainty in calibration data derived by the rating curve method may be of the same relevance as rainfall-runoff model parameters themselves. A conceptual limitation of the approach presented is that it is limited to water level predictions. Nevertheless, regarding flood level predictions, the Bayesian framework seems very promising because it (i) enables the modeler to incorporate informal knowledge from easily accessible information and (ii) bet- ter assesses the individual error contributions. Especially the latter is important to improve the predictive capability of hy- drological models.

Curve Number Estimation for a Small Urban Catchment from Recorded Rainfall-Runoff Events

Abstract: 7 8 Abstract 11 Runoff estimation is a key component in various hydrological considerations. Estimation 12 of storm runoff is especially important for the effective design of hydraulic and road 13 structures, for the flood flow management, as well as for the analysis of land use changes, 14 i.e. urbanization or low impact development of urban areas . The curve number (CN) 15

Soil erosion prediction using the Revised Universal Soil Loss Equation (RUSLE) in a GIS framework, Chania, Northwestern Crete, Greece

Abstract: Soil erosion is a growing problem in southern Greece and particularly in the island of Crete, the biggest Greek island with great agricultural activity. Soil erosion not only decreases agricultural productivity, but also reduces the water availability. In the current study, an effort to predict potential annual soil loss has been conducted. For the prediction, the Revised Universal Soil Loss Equation (RUSLE) has been adopted in a Geographical Information System framework. The RUSLE factors were calculated (in the form of raster layers) for the nine major watersheds which cover the northern part of the Chania Prefecture. The R-factor was calculated from monthly and annual precipitation data. The K-factor was estimated using soil maps available from the Soil Geographical Data Base of Europe at a scale of 1:1,000,000. The LS-factor was calculated from a 30-m digital elevation model. The C-factor was calculated using Remote Sensing techniques. The P-factor in absence of data was set to 1. The results show that an extended part of the area is undergoing severe erosion. The mean annual soil loss is predicted up to ∼200 (t/ha year−1) for some watersheds showing extended erosion and demanding the attention of local administrators.

Global rainfall erosivity assessment based on high-temporal resolution rainfall records

Abstract: The exposure of the Earth’s surface to the energetic input of rainfall is one of the key factors controlling water erosion. While water erosion is identified as the most serious cause of soil degradation globally, global patterns of rainfall erosivity remain poorly quantified and estimates have large uncertainties. This hampers the implementation of effective soil degradation mitigation and restoration strategies. Quantifying rainfall erosivity is challenging as it requires high temporal resolution(<30 min) and high fidelity rainfall recordings. We present the results of an extensive global data collection effort whereby we estimated rainfall erosivity for 3,625 stations covering 63 countries. This first ever Global Rainfall Erosivity Database was used to develop a global erosivity map at 30 arc-seconds(~1 km) based on a Gaussian Process Regression(GPR). Globally, the mean rainfall erosivity was estimated to be 2,190 MJ mm ha−1 h−1 yr−1, with the highest values in South America and the Caribbean countries, Central east Africa and South east Asia. The lowest values are mainly found in Canada, the Russian Federation, Northern Europe, Northern Africa and the Middle East. The tropical climate zone has the highest mean rainfall erosivity followed by the temperate whereas the lowest mean was estimated in the cold climate zone.

A process-based typology of hydrological drought

Abstract: Hydrological drought events have very differ- ent causes and effects. Classifying these events into dis- tinct types can be useful for both science and manage- ment. We propose a hydrological drought typology that is based on governing drought propagation processes de- rived from catchment-scale drought analysis. In this ty- pology six hydrological drought types are distinguished, i.e. (i) classical rainfall deficit drought, (ii) rain-to-snow- season drought, (iii) wet-to-dry-season drought, (iv) cold snow season drought, (v) warm snow season drought , and (vi) composite drought. The processes underlying these drought types are the result of the interplay of temperature and precipitation at catchment scale in different seasons. As a test case, about 125 groundwater droughts and 210 dis- charge droughts in five contrasting headwater catchments in Europe have been classified. The most common drought type in all catchments was the classical rainfall deficit drought (almost 50 % of all events), but in the selected catchments these were mostly minor events. If only the five most severe drought events of each catchment are considered, a shift to- wards more rain-to-snow-season droughts , warm snow sea- son droughts, and composite droughts was found. The oc- currence of hydrological drought types is determined by cli- mate and catchment characteristics. The drought typology is transferable to other catchments, including outside Europe, because it is generic and based upon processes that occur around the world. A general framework is proposed to iden- tify drought type occurrence in relation to climate and catch- ment characteristics.